mlsanityData to 7 Oct 2026

Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning

arXiv 2101.02349 ↗First posted 7 Jan 2021cs.AI cs.MAcited by 7

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Abstract (arXiv)

In this work, we consider the problem of computing optimal actions for Reinforcement Learning (RL) agents in a co-operative setting, where the objective is to optimize a common goal. However, in many real-life applications, in addition to optimizing the goal, the agents are required to satisfy certain constraints specified on their actions. Under this setting, the objective of the agents is to not only learn the actions that optimize the common objective but also meet the specified constraints. In recent times, the Actor-Critic algorithm with an attention mechanism has been successfully applied to obtain optimal actions for RL agents in multi-agent environments. In this work, we extend this algorithm to the constrained multi-agent RL setting. The idea here is that optimizing the common goal and satisfying the constraints may require different modes of attention. By incorporating different attention modes, the agents can select useful information required for optimizing the objective and satisfying the constraints separately, thereby yielding better actions. Through experiments on benchmark multi-agent environments, we show the effectiveness of our proposed algorithm.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

16 of 35 references.

References · 16

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  4. 2017Constrained Policy Optimizationcited by 928
  5. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  6. 2018Reward Constrained Policy Optimizationcited by 397
  7. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  8. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  9. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 180
  10. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  11. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  12. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 122
4 more
  1. 2018Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learningcited by 61
  2. 2018Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPGcited by 20
  3. 2019A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillationcited by 12
  4. 2019Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learningcited by 5

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 5.

7 papers.

  1. 2023Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learningcited by 13
  2. 2023Provably Learning Nash Policies in Constrained Markov Potential Gamescited by 7
  3. 2021SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environmentscited by 2
  4. 2022Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learningcited by 0
  5. 2023Actor-Director-Critic: A Novel Deep Reinforcement Learning Frameworkcited by 0
  6. 2023Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learningcited by 0
  7. 2023DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraintscited by 0